In the advent of deregulation, load forecasting has taken center stage in providing a means of scheduling energy generation in a marketplace where energy demand may at times appear random. System operators, market operators, transmission owners, and other power industry participants all require a forecasting system and methodology which is fast and accurate in order to operate a reliable system and to remain profitable. Accordingly, forecasting is a necessity in a free market.
However, the restructuring in the power industry from a regulated monopoly to a free market system in which transmission operators are required to operate under regulatory scrutiny and in a fair manner has further added a level of complexity in meeting energy demands. Often the manner in which market participants are required to operate is in direct conflict with technical and economic constraints. These constraints are imposed by regulatory agencies, economic concerns, and equipment performance limitations.
At the moment, operators provide regulatory agencies with schedule information detailing the quantity of energy and the time that energy will be produced. These schedules of energy vary over the course of a year, month, week, day, hour and other intervals of time such as seasons and special days such as holidays and weekends. Despite knowing that such energy requirements vary considerably at times, operators are often tasked with the burden of meeting demand for real-time and unanticipated shortage in energy. Meeting these unanticipated demands is often the cause of increased energy costs. Alternatively, decreases in energy costs may be the result of having to provide less energy when a glut of unanticipated energy exists in the marketplace.
As readily apparent, there is a significant need for a method and system which optimizes the performance of a power system performance by predicting in the short term the expect demand energy. The present invention is a solution to the needs of energy producers which must control their production capacity to meet regulatory requirements and to minimize costs and optimize profitability by utilizing field data, historical load and forecast data as well forecasts from other forecasting techniques.